Data Analysis

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Data Analysis

What is Data analysis ?
Cleaning, converting, and modelling data to identify usable information for business decision-making is defined as data analysis. Data analysis' goal is to extract usable information from data and make decisions based on that knowledge.
A basic example of data analysis is when we make a decision in our daily lives, we consider what happened the last time we made that decision or what will happen if we make that decision. This is nothing more than looking backwards or forwards in time and making decisions depending on our findings. We do this by gathering memories from the past or fantasising about the future. So that's all there is to data analysis. Data analysis is what an analyst performs now for business goals.

What is Data analysis process ?
As the amount and complexity of data available to businesses grows, so does the need for an effective and efficient approach to extract value from it. Typically, the analysis approach goes through multiple iterative stages. Let's look at each one in more detail.

Determine the business question you want to address. What is the company's goal in terms of solving a problem? What are you going to measure, and how are you going to measure it?

Collect the raw data sets you'll need to answer the question you've identified. Internal sources, such as a company's client relationship management (CRM) software, or secondary sources, such as government records or social media application programming interfaces, may be used to collect data (APIs).

To prepare the data for analysis, clean it. Purging duplicate and anomalous data, resolving inconsistencies, standardising data structure and format, and dealing with white spaces and other syntax problems are all common examples of what this entails.

Analyze the information. You can start to uncover trends, correlations, outliers, and changes in the data by modifying it with various data analysis techniques and tools. During this step, you might use data mining to find trends in databases or data visualisation software to help transform data into a graphical format that's easier to grasp.

Interpret your analysis' findings to determine how effectively the data answered your initial query. What conclusions can you draw from the information? What constraints do your conclusions have?

What is Data Analysis: Types of Data Analysis ?

There are many types of data analysis available today, and they're widely used in the tech and business realms. They are as follows:

Diagnostic Analysis: Diagnostic analysis provides a response to the query, "What happened?" Analysts employ diagnostic analysis to find patterns in data using insights gained from statistical analysis (more on that later!). In an ideal world, the analysts would uncover comparable patterns that existed in the past and, perhaps, employ those solutions to fix the current problems.

Predictive analysis answers the question, "What is most likely to happen?" Analysts forecast future occurrences by using patterns identified in previous data as well as present happenings. While there is no such thing as 100 percent accurate predicting, the analysts' chances improve if they have a wealth of precise data and the discipline to thoroughly analyse it.

Prescriptive Analysis: Prescriptive analysis combines all of the insights gathered from the previous data analysis techniques. Sometimes a problem can't be handled with just one sort of analysis, and instead necessitates a combination of approaches.

Statistical analysis provides a solution to the query, "What happened?" Data collection, analysis, modelling, interpretation, and presentation utilising dashboards are all included in this investigation. The statistical analysis is divided into two sections:

Descriptive analysis is used to analyse numerical data that is either entire or a subset of it. In continuous data, it shows means and deviations; in categorical data, it shows percentages and frequencies.

Inferential: Samples produced from complete data are used in inferential analysis. By selecting alternative samplings, an analyst can arrive at different conclusions from the same entire data set.

Text analysis, often known as "data mining," use databases and data mining software to uncover patterns in massive datasets. It converts unstructured data into actionable business information. Text analysis is, without a doubt, the simplest and most direct approach of data analysis.

Why is Data Analysis Important?
Here are some of the reasons why data analysis is so important in today's corporate world.

Better Customer Targeting: You don't want to waste your company's time, energy, and money creating advertising campaigns for demographic groups who aren't interested in the products and services you offer. Data analysis can assist you in determining where your advertising efforts should be focused.

You'll Have a Better Understanding of Your Target Customers: Data analysis monitors how well your items and marketing are performing among your target demographic. Your company can gain a better understanding of your target audience's spending habits, disposable income, and most likely areas of interest through data analysis. This information aids firms in setting prices, determining the length of ad campaigns, and even forecasting the amount of items required.

Reduce Operational Costs: Data analysis reveals which sections of your organisation require additional resources and funding, as well as which areas are underperforming and should be trimmed back or eliminated entirely.

Improved Problem-Solving Techniques: Well-informed decisions are more likely to be effective. Data gives information to businesses. You can see where this path is taking you. Data analysis aids organisations in making the best decisions and avoiding costly blunders.

You Get More Accurate Data: You need data to make informed judgments, but there's more to it than that. The information in question must be correct. Data analysis assists businesses in obtaining relevant, accurate data that may be used to design future marketing strategies, business plans, and realign the company's vision or mission.

What Is the Data Analysis Process?

Gathering all of the information, processing it, studying the data, and using it to uncover patterns and other insights are all part of the data analysis process, or data analysis processes. The procedure entails the following steps:

Gathering Data Requirements: Consider why you're undertaking this analysis, what style of data analysis you'd like to utilise, and what data you'll be studying.

Data Collection: Now is the time to collect data from your sources, guided by the needs you've specified. Case studies, surveys, interviews, questionnaires, direct observation, and focus groups are examples of sources. Make sure the data you've gathered is organised before you start analysing it.

Data Cleaning: You won't need all of the data you collect, so it's time to clean it up. White spaces, duplicate data, and simple errors are all removed during this step. Before sending the data to be analysed, it must first be cleaned.

Data analysis software and other tools are used to assist you analyse and understand the data and come to conclusions. Excel, Python, R, Looker, Rapid Miner, Chartio, Metabase, Redash, and Microsoft Power BI are some of the data analysis tools available.

Data Interpretation: Now that you have your results, you must interpret them and determine the best course of action based on what you've learned.

Data Visualization: "Graphically exhibit your information in a way that people can read and comprehend it" is a fancy way of stating "visually show your information in a way that people can read and understand it." Charts, graphs, maps, bullet points, and a variety of other ways can be used. By allowing you to compare information and detect relationships, visualisation assists you in gaining useful insights.

Researchers can use a variety of tools in data analysis, including descriptive statistics, inferential analysis, and quantitative analysis.

Data Analysis Methods
There are a variety of data analysis techniques accessible, but they all fall into one of two categories: qualitative or quantitative analysis.

Qualitative Analysis  Methods
Data is gathered using words, symbols, drawings, and observations in the qualitative data analysis approach. Statistics aren't used in this procedure. The following are some of the most common qualitative methods:
For assessing behavioural and linguistic data, use content analysis.
Narrative Analysis is a technique for working with information gleaned from interviews, diaries, and surveys.
Grounded Theory is a method for constructing causal explanations for a specific event by researching and extrapolating from one or more previous cases.

Quantitative Analysis Methods
Statistical data analysis methods capture raw data and turn it into numerical data, which is known as quantitative data analysis. Methods of quantitative analysis include:
For a data collection or demography, hypothesis testing is used to determine whether a certain hypothesis or theory is true.
By dividing the sum of a list of numbers by the number of items on the list, the mean, or average, determines the overall tendency of a subject.

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